if(!require(correlation)){install.packages("correlation"); library(correlation)}
## Loading required package: correlation
if(!require(car)){install.packages("car"); library(car)}
## Loading required package: car
## Loading required package: carData
if(!require(mgcv)){install.packages("mgcv"); library(mgcv)}
## Loading required package: mgcv
## Loading required package: nlme
## This is mgcv 1.9-0. For overview type 'help("mgcv-package")'.
if(!require(rpart)){install.packages("rpart"); library(rpart)}
## Loading required package: rpart
if(!require(ggplot2)){install.packages("ggplot2"); library(ggplot2)}
## Loading required package: ggplot2
if(!require(gridExtra)){install.packages("gridExtra"); library(gridExtra)}
## Loading required package: gridExtra
if(!require(lme4)){install.packages("lme4"); library(lme4)}
## Loading required package: lme4
## Loading required package: Matrix
## 
## Attaching package: 'lme4'
## The following object is masked from 'package:nlme':
## 
##     lmList
if(!require(Matrix)){install.packages("Matrix"); library(Matrix)}
if(!require(lmtest)){install.packages("lmtest"); library(lmtest)}
## Loading required package: lmtest
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
if(!require(gamm4)){install.packages("gamm4"); library(gamm4)}
## Loading required package: gamm4
## This is gamm4 0.2-6
if(!require(sjPlot)){install.packages("sjPlot"); library(sjPlot)}
## Loading required package: sjPlot
if(!require(sjmisc)){install.packages("sjmisc"); library(sjmisc)}
## Loading required package: sjmisc
## Learn more about sjmisc with 'browseVignettes("sjmisc")'.
if(!require(sjlabelled)){install.packages("sjlabelled"); library(sjlabelled)}
## Loading required package: sjlabelled
## 
## Attaching package: 'sjlabelled'
## The following object is masked from 'package:ggplot2':
## 
##     as_label
if(!require(performance)){install.packages("performance"); library(performance)}
## Loading required package: performance
if(!require(glmmTMB)){install.packages("glmmTMB"); library(glmmTMB)}
## Loading required package: glmmTMB
## Warning in checkDepPackageVersion(dep_pkg = "TMB"): Package version inconsistency detected.
## glmmTMB was built with TMB version 1.9.10
## Current TMB version is 1.9.11
## Please re-install glmmTMB from source or restore original 'TMB' package (see '?reinstalling' for more information)
if(!require(DHARMa)){install.packages("DHARMa"); library(DHARMa)}
## Loading required package: DHARMa
## This is DHARMa 0.4.6. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa')

Import Dataset

#Import data
week_kuds <- read.csv("week_kuds2.2.csv", sep=";") #has the wrong week values
week_kuds3 <- read.csv("week_kuds1 - usar.csv", sep=";") #has the right week values
week_kuds$Week <- week_kuds3$Week #substitute the wrong for the right week in the dataset
#create week variable without the year
names(week_kuds)[names(week_kuds) == "Week"] <- "WeekYear"
week_kuds$Week <- substr(week_kuds$WeekYear, 1, 2)
#create year variable without the week
week_kuds$Year <- substr(week_kuds$WeekYear, 4, 7)

week_kuds$File <- as.factor(week_kuds$File)
week_kuds$Species <- as.factor(week_kuds$Species)
week_kuds$Transmitter <- as.factor(week_kuds$Transmitter)
week_kuds$KUD50 <- as.numeric(week_kuds$KUD50)
week_kuds$KUD95 <- as.numeric(week_kuds$KUD95)
week_kuds$Habitat <- as.factor(week_kuds$Habitat)
week_kuds$Migration <- as.factor(week_kuds$Migration)
week_kuds$ComImport <- as.factor(week_kuds$ComImport)
week_kuds$Length_cm <- as.numeric(week_kuds$Length_cm)
week_kuds$LengthStd <- as.numeric(week_kuds$LengthStd)
week_kuds$BodyMass <- as.numeric(week_kuds$BodyMass)
week_kuds$BodyMassStd <- as.numeric(week_kuds$BodyMassStd)
week_kuds$Longevity <- as.numeric(week_kuds$Longevity)
week_kuds$Vulnerability <- as.numeric(week_kuds$Vulnerability)
week_kuds$Troph <- as.numeric(week_kuds$Troph)
week_kuds$ReceiverDensity <- as.numeric(week_kuds$ReceiverDensity)
week_kuds$MonitArea_km2 <- as.numeric(week_kuds$MonitArea_km2)
week_kuds$MCP_km2 <- as.numeric(week_kuds$MCP_km2)
week_kuds$NReceivers <- as.numeric(week_kuds$NReceivers)
week_kuds$MaxDistReceivers <- as.numeric(week_kuds$MaxDistReceivers)
week_kuds$MaxLength <- as.numeric(week_kuds$MaxLength)
week_kuds$MaxBodyMass <- as.numeric(week_kuds$MaxBodyMass)
week_kuds$a <- as.numeric(week_kuds$a)
week_kuds$b <- as.numeric(week_kuds$b)
week_kuds$Week <- as.factor(week_kuds$Week)
week_kuds$Year <- as.factor(week_kuds$Year)
week_kuds$Spawn <- as.factor(week_kuds$Spawn)

week_kuds$Spawn <- with(week_kuds, ifelse((SpawnSeason == "SS" & Week %in% c("11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40")) |
                          (SpawnSeason == "A" & Week %in% c("41", "42", "43", "44", "45", "46", "47", "48", "49", "50")) |
                          (SpawnSeason == "W" & Week %in% c("51", "52", "53", "54", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10")),
                          "yes", "no"))
week_kuds$SpawnSeason <- as.factor(week_kuds$SpawnSeason)
boxplot(KUD95 ~ Spawn, data= week_kuds, col="deepskyblue")

boxplot(KUD50 ~ Spawn, data= week_kuds, col="green2")

#Comparar as médias dos KUDs dos individuos que se encontravam em época reprodutiva ou não

#escolhemos o teste wilcox porque não assume normalidade nos dados e é útil para grandes e pequenas amostras
wilcox.test(week_kuds$KUD95~week_kuds$Spawn)  #de acordo com o teste realizado parece não haver evidências para afirmar que a home range varia consoante a Epoca Reprodutiva, visto que o p-value toma o valor de 0.5623 que é maior do que o nivel de significância 0.05
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  week_kuds$KUD95 by week_kuds$Spawn
## W = 81731513, p-value = 0.5623
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(week_kuds$KUD50~week_kuds$Spawn) #de acordo com o teste realizado parece não haver evidências para afirmar que a core area varia consoante a Epoca Reprodutiva, visto que o p-value toma o valor de 0.9972 que é maior do que o nivel de significância 0.05
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  week_kuds$KUD50 by week_kuds$Spawn
## W = 81393886, p-value = 0.9972
## alternative hypothesis: true location shift is not equal to 0
glmm_total_kud95 <- glmmTMB(KUD95 ~ Spawn + (1|File) + (1|Transmitter), data=week_kuds, family = Gamma(link="log"))
summary(glmm_total_kud95)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | File) + (1 | Transmitter)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9685.9   9726.6  -4837.9   9675.9    25607 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  File        (Intercept) 0.11301  0.3362  
##  Transmitter (Intercept) 0.07224  0.2688  
## Number of obs: 25612, groups:  File, 48; Transmitter, 850
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0747 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 0.077092   0.051262   1.504    0.133    
## Spawnyes    0.055785   0.003804  14.663   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
glmm_total_kud50 <- glmmTMB(KUD50 ~ Spawn + (1|File) + (1|Transmitter), data=week_kuds, family = Gamma(link="log"))
summary(glmm_total_kud50)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | File) + (1 | Transmitter)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76507.0 -76466.2  38258.5 -76517.0    25607 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  File        (Intercept) 0.08314  0.2883  
##  Transmitter (Intercept) 0.06028  0.2455  
## Number of obs: 25612, groups:  File, 48; Transmitter, 850
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0603 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.508216   0.044203  -34.12   <2e-16 ***
## Spawnyes     0.046300   0.003413   13.56   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Analisys by File
# Divide dataset by 'File'
split_spawnFile <- split(week_kuds, week_kuds$File)

# Function to use the wilcoxon test in each sub-dataframe
wilcox_test_kud95 <- function(data) {
  # Verify if Spawn has exactly 2 levels (yes and no)
  if(length(unique(data$Spawn)) == 2) {
    test_result <- wilcox.test(KUD95 ~ Spawn, data = data)
    return(test_result$p.value)
  } else {
    return(NA)  # Return NA if not
  }
}

# Apply the function and get the p-values
p_values <- lapply(split_spawnFile, wilcox_test_kud95)
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
# Exhibit p-values
print(p_values)
## $Dactylopterus_volitans
## [1] NA
## 
## $Dentex_dentex1
## [1] 0.01030334
## 
## $Dentex_dentex2
## [1] 0.1078085
## 
## $Dicentrarchus_labrax1
## [1] 1.450456e-05
## 
## $Dicentrarchus_labrax2
## [1] 0.0001058427
## 
## $Diplodus_cervinus
## [1] 0.7975494
## 
## $Diplodus_sargus1
## [1] 0.1054672
## 
## $Diplodus_sargus2
## [1] 0.00252126
## 
## $Diplodus_sargus3
## [1] 0.002851269
## 
## $Diplodus_sargus4
## [1] 0.224059
## 
## $Diplodus_sargus5
## [1] 0.2519681
## 
## $Diplodus_sargus6
## [1] 0.6146928
## 
## $Diplodus_vulgaris1
## [1] 0.1348518
## 
## $Diplodus_vulgaris2
## [1] 0.3333333
## 
## $Epinephelus_marginatus1
## [1] 0.08732378
## 
## $Epinephelus_marginatus2
## [1] 0.8344767
## 
## $Epinephelus_marginatus3
## [1] 0.2898365
## 
## $Epinephelus_marginatus4
## [1] 2.725643e-05
## 
## $Gadus_morhua1
## [1] 3.51374e-06
## 
## $Gadus_morhua2
## [1] 0.3779574
## 
## $Gadus_morhua3
## [1] 3.646825e-09
## 
## $Labrus_bergylta
## [1] 1.611826e-07
## 
## $Lichia_amia
## [1] 0.03284102
## 
## $Lithognathus_mormyrus
## [1] NA
## 
## $Pagellus_erythrinus
## [1] NA
## 
## $Pagrus_pagrus1
## [1] 0.1970555
## 
## $Pagrus_pagrus2
## [1] 0.3868507
## 
## $Pomatomus_saltatrix
## [1] 0.90633
## 
## $Pseudocaranx_dentex
## [1] 0.074285
## 
## $Sciaena_umbra1
## [1] 0.100855
## 
## $Sciaena_umbra2
## [1] 0.0530303
## 
## $Scorpaena_porcus
## [1] 0.02050939
## 
## $Scorpaena_scrofa1
## [1] 0.5053349
## 
## $Scorpaena_scrofa2
## [1] 4.234985e-05
## 
## $Seriola_dumerili
## [1] 2.94866e-12
## 
## $Seriola_rivoliana
## [1] 4.398831e-06
## 
## $Serranus_atricauda
## [1] 6.274069e-08
## 
## $Serranus_cabrilla
## [1] NA
## 
## $Serranus_scriba
## [1] 0.8412007
## 
## $Solea_senegalensis
## [1] 0.03581104
## 
## $Sparisoma_cretense
## [1] 0.3443516
## 
## $Sparus_aurata1
## [1] 0.434566
## 
## $Sparus_aurata2
## [1] 0.1317029
## 
## $Sphyraena_viridensis1
## [1] 0.0005515177
## 
## $Sphyraena_viridensis2
## [1] 2.323167e-15
## 
## $Spondyliosoma_cantharus
## [1] 5.783145e-05
## 
## $Umbrina_cirrosa
## [1] NA
## 
## $Xyrichtys_novacula
## [1] NA
p_values<- t(as.data.frame(p_values))

# Function to use the wilcoxon test in each sub-dataframe
wilcox_test_kud50 <- function(data) {
  # Verify if Spawn has exactly 2 levels (yes and no)
  if(length(unique(data$Spawn)) == 2) {
    test_result <- wilcox.test(KUD50 ~ Spawn, data = data)
    return(test_result$p.value)
  } else {
    return(NA)  # Return NA if not
  }
}

# Apply the function and get the p-values
p_values <- lapply(split_spawnFile, wilcox_test_kud50)
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
# Exhibit p-values
print(p_values)
## $Dactylopterus_volitans
## [1] NA
## 
## $Dentex_dentex1
## [1] 0.0006766402
## 
## $Dentex_dentex2
## [1] 0.07389496
## 
## $Dicentrarchus_labrax1
## [1] 0.0007138528
## 
## $Dicentrarchus_labrax2
## [1] 0.002072089
## 
## $Diplodus_cervinus
## [1] 0.1787112
## 
## $Diplodus_sargus1
## [1] 0.05129045
## 
## $Diplodus_sargus2
## [1] 0.009404127
## 
## $Diplodus_sargus3
## [1] 0.005536751
## 
## $Diplodus_sargus4
## [1] 0.08955937
## 
## $Diplodus_sargus5
## [1] 0.1154605
## 
## $Diplodus_sargus6
## [1] 0.8877892
## 
## $Diplodus_vulgaris1
## [1] 0.09801128
## 
## $Diplodus_vulgaris2
## [1] 0.3333333
## 
## $Epinephelus_marginatus1
## [1] 0.0007550945
## 
## $Epinephelus_marginatus2
## [1] 0.8899593
## 
## $Epinephelus_marginatus3
## [1] 0.360626
## 
## $Epinephelus_marginatus4
## [1] 1.858381e-05
## 
## $Gadus_morhua1
## [1] 8.959164e-05
## 
## $Gadus_morhua2
## [1] 0.6036561
## 
## $Gadus_morhua3
## [1] 4.300043e-09
## 
## $Labrus_bergylta
## [1] 1.68144e-08
## 
## $Lichia_amia
## [1] 0.2620757
## 
## $Lithognathus_mormyrus
## [1] NA
## 
## $Pagellus_erythrinus
## [1] NA
## 
## $Pagrus_pagrus1
## [1] 0.04727474
## 
## $Pagrus_pagrus2
## [1] 0.2663605
## 
## $Pomatomus_saltatrix
## [1] 0.9564324
## 
## $Pseudocaranx_dentex
## [1] 0.06130092
## 
## $Sciaena_umbra1
## [1] 0.1403615
## 
## $Sciaena_umbra2
## [1] 0.259324
## 
## $Scorpaena_porcus
## [1] 0.01589705
## 
## $Scorpaena_scrofa1
## [1] 0.8384118
## 
## $Scorpaena_scrofa2
## [1] 0.0001346187
## 
## $Seriola_dumerili
## [1] 7.89286e-12
## 
## $Seriola_rivoliana
## [1] 0.1443612
## 
## $Serranus_atricauda
## [1] 1.429118e-09
## 
## $Serranus_cabrilla
## [1] NA
## 
## $Serranus_scriba
## [1] 0.8428269
## 
## $Solea_senegalensis
## [1] 0.1996681
## 
## $Sparisoma_cretense
## [1] 0.6576976
## 
## $Sparus_aurata1
## [1] 0.6422533
## 
## $Sparus_aurata2
## [1] 0.005342033
## 
## $Sphyraena_viridensis1
## [1] 0.001669869
## 
## $Sphyraena_viridensis2
## [1] 6.294566e-07
## 
## $Spondyliosoma_cantharus
## [1] 0.0001120269
## 
## $Umbrina_cirrosa
## [1] NA
## 
## $Xyrichtys_novacula
## [1] NA
p_values<- t(as.data.frame(p_values))
#Glmm KUD95 for each File

data_dentex_dentex1 <- subset(week_kuds, File == "Dentex_dentex1")

glmm_dentex_dentex1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dentex_dentex1, family = Gamma(link="log"))
summary(glmm_dentex_dentex1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex1
## 
##      AIC      BIC   logLik deviance df.resid 
##      3.3     21.9      2.4     -4.7      774 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03892  0.1973  
## Number of obs: 778, groups:  Transmitter, 19
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0384 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.11417    0.04733   2.412   0.0159 *  
## Spawnyes     0.06093    0.01442   4.227 2.37e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex1$KUD95 ~ data_dentex_dentex1$Spawn)

#################################################################################
data_dentex_dentex2 <- subset(week_kuds, File == "Dentex_dentex2")

glmm_dentex_dentex2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dentex_dentex2, family = Gamma(link="log"))
summary(glmm_dentex_dentex2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex2
## 
##      AIC      BIC   logLik deviance df.resid 
##   1008.2   1025.8   -500.1   1000.2      595 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1173   0.3426  
## Number of obs: 599, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.138 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.28149    0.09143   3.079  0.00208 ** 
## Spawnyes     0.15458    0.03177   4.865 1.14e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex2$KUD95 ~ data_dentex_dentex2$Spawn)

#################################################################################
data_dicentrarchus_labrax1 <- subset(week_kuds, File == "Dicentrarchus_labrax1")

glmm_dicentrarchus_labrax1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax1, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax1
## 
##      AIC      BIC   logLik deviance df.resid 
##    753.4    772.4   -372.7    745.4      850 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1006   0.3171  
## Number of obs: 854, groups:  Transmitter, 93
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0931 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.16255    0.03588   4.531 5.88e-06 ***
## Spawnyes    -0.04691    0.06031  -0.778    0.437    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax1$KUD95 ~ data_dicentrarchus_labrax1$Spawn)

#################################################################################
data_dicentrarchus_labrax2 <- subset(week_kuds, File == "Dicentrarchus_labrax2")

glmm_dicentrarchus_labrax2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax2, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax2
## 
##      AIC      BIC   logLik deviance df.resid 
##   1620.9   1638.7   -806.4   1612.9      633 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.2229   0.4722  
## Number of obs: 637, groups:  Transmitter, 28
## 
## Dispersion estimate for Gamma family (sigma^2): 0.188 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.62269    0.09675   6.436 1.22e-10 ***
## Spawnyes     0.26338    0.03995   6.593 4.31e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax2$KUD95 ~ data_dicentrarchus_labrax2$Spawn)

#################################################################################
data_diplodus_cervinus <- subset(week_kuds, File == "Diplodus_cervinus")

glmm_diplodus_cervinus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_cervinus, family = Gamma(link="log"))
summary(glmm_diplodus_cervinus)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_cervinus
## 
##      AIC      BIC   logLik deviance df.resid 
##    164.8    175.0    -78.4    156.8       90 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.147    0.3834  
## Number of obs: 94, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.15 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.31443    0.20324   1.547  0.12185   
## Spawnyes    -0.25631    0.09128  -2.808  0.00498 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_cervinus$KUD95 ~ data_diplodus_cervinus$Spawn)

#################################################################################
data_diplodus_sargus1 <- subset(week_kuds, File == "Diplodus_sargus1")

glmm_diplodus_sargus1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus1, family = Gamma(link="log"))
summary(glmm_diplodus_sargus1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus1
## 
##      AIC      BIC   logLik deviance df.resid 
##    143.7    159.1    -67.8    135.7      347 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07295  0.2701  
## Number of obs: 351, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0723 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.042330   0.074203   0.571    0.568
## Spawnyes    -0.009141   0.033723  -0.271    0.786
boxplot(data_diplodus_sargus1$KUD95 ~ data_diplodus_sargus1$Spawn)

#################################################################################
data_diplodus_sargus2 <- subset(week_kuds, File == "Diplodus_sargus2")

glmm_diplodus_sargus2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus2, family = Gamma(link="log"))
summary(glmm_diplodus_sargus2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus2
## 
##      AIC      BIC   logLik deviance df.resid 
##   -653.2   -635.2    330.6   -661.2      656 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.01889  0.1374  
## Number of obs: 660, groups:  Transmitter, 20
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0237 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.02555    0.03412   0.749   0.4539  
## Spawnyes    -0.02520    0.01480  -1.703   0.0886 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus2$KUD95 ~ data_diplodus_sargus2$Spawn)

#################################################################################
data_diplodus_sargus3 <- subset(week_kuds, File == "Diplodus_sargus3")

glmm_diplodus_sargus3 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus3, family = Gamma(link="log"))
summary(glmm_diplodus_sargus3)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus3
## 
##      AIC      BIC   logLik deviance df.resid 
##   -177.8   -168.2     92.9   -185.8       76 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0006858 0.02619 
## Number of obs: 80, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00797 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.22252    0.01878 -11.851  < 2e-16 ***
## Spawnyes     0.08364    0.02016   4.148 3.36e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus3$KUD95 ~ data_diplodus_sargus3$Spawn)

#################################################################################
data_diplodus_sargus4 <- subset(week_kuds, File == "Diplodus_sargus4")

glmm_diplodus_sargus4 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus4, family = Gamma(link="log"))
summary(glmm_diplodus_sargus4)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus4
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1780.1  -1758.9    894.1  -1788.1     1470 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02426  0.1558  
## Number of obs: 1474, groups:  Transmitter, 41
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0172 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.055328   0.025343  -2.183    0.029 *
## Spawnyes    -0.004623   0.006936  -0.666    0.505  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus4$KUD95 ~ data_diplodus_sargus4$Spawn)

#################################################################################
data_diplodus_sargus5 <- subset(week_kuds, File == "Diplodus_sargus5")

glmm_diplodus_sargus5 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus5, family = Gamma(link="log"))
summary(glmm_diplodus_sargus5)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus5
## 
##      AIC      BIC   logLik deviance df.resid 
##    291.3    311.3   -141.6    283.3     1098 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02454  0.1566  
## Number of obs: 1102, groups:  Transmitter, 73
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0703 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.03582    0.02336   1.533    0.125
## Spawnyes    -0.01763    0.01790  -0.985    0.325
boxplot(data_diplodus_sargus5$KUD95 ~ data_diplodus_sargus5$Spawn)

#################################################################################
data_diplodus_sargus6 <- subset(week_kuds, File == "Diplodus_sargus6")

glmm_diplodus_sargus6 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus6, family = Gamma(link="log"))
summary(glmm_diplodus_sargus6)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus6
## 
##      AIC      BIC   logLik deviance df.resid 
##    -37.4    -30.5     22.7    -45.4       37 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1828   0.4275  
## Number of obs: 41, groups:  Transmitter, 6
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0165 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.06001    0.17875   0.336    0.737
## Spawnyes    -0.02549    0.05101  -0.500    0.617
boxplot(data_diplodus_sargus6$KUD95 ~ data_diplodus_sargus6$Spawn)

#################################################################################
data_diplodus_vulgaris1 <- subset(week_kuds, File == "Diplodus_vulgaris1")

glmm_diplodus_vulgaris1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris1, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris1
## 
##      AIC      BIC   logLik deviance df.resid 
##     -7.4      0.0      7.7    -15.4       42 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03967  0.1992  
## Number of obs: 46, groups:  Transmitter, 9
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0369 
## 
## Conditional model:
##               Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.0009159  0.0769672   0.012   0.9905  
## Spawnyes    -0.1871862  0.1024773  -1.827   0.0678 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris1$KUD95 ~ data_diplodus_vulgaris1$Spawn)

#################################################################################
data_diplodus_vulgaris2 <- subset(week_kuds, File == "Diplodus_vulgaris2")

glmm_diplodus_vulgaris2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris2, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris2
## 
##      AIC      BIC   logLik deviance df.resid 
##     -7.7    -10.1      7.8    -15.7        0 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.006177 0.07859 
## Number of obs: 4, groups:  Transmitter, 2
## 
## Dispersion estimate for Gamma family (sigma^2): 7.18e-05 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.53980    0.05590    9.66   <2e-16 ***
## Spawnyes    -0.72481    0.01044  -69.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris2$KUD95 ~ data_diplodus_vulgaris2$Spawn)

#################################################################################
data_epinephelus_marginatus1 <- subset(week_kuds, File == "Epinephelus_marginatus1")

glmm_epinephelus_marginatus1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus1, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3878.8  -3856.3   1943.4  -3886.8     2051 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.004326 0.06577 
## Number of obs: 2055, groups:  Transmitter, 11
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0131 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.232142   0.020292 -11.440  < 2e-16 ***
## Spawnyes     0.035552   0.005102   6.969  3.2e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus1$KUD95 ~ data_epinephelus_marginatus1$Spawn)

#################################################################################
data_epinephelus_marginatus2 <- subset(week_kuds, File == "Epinephelus_marginatus2")

glmm_epinephelus_marginatus2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus2, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1926.5  -1910.2    967.3  -1934.5      433 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 5.013e-05 0.00708 
## Number of obs: 437, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00114 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.260076   0.002685  -96.87  < 2e-16 ***
## Spawnyes     0.010431   0.003580    2.91  0.00358 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus2$KUD95 ~ data_epinephelus_marginatus2$Spawn)

#################################################################################
data_epinephelus_marginatus3 <- subset(week_kuds, File == "Epinephelus_marginatus3")

glmm_epinephelus_marginatus3 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus3, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus3)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus3
## 
##      AIC      BIC   logLik deviance df.resid 
##   -689.6   -675.9    348.8   -697.6      223 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0002354 0.01534 
## Number of obs: 227, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00425 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.249606   0.010156 -24.576  < 2e-16 ***
## Spawnyes     0.023242   0.008761   2.653  0.00798 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus3$KUD95 ~ data_epinephelus_marginatus3$Spawn)

#################################################################################
data_epinephelus_marginatus4 <- subset(week_kuds, File == "Epinephelus_marginatus4")

glmm_epinephelus_marginatus4 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus4, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus4)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus4
## 
##      AIC      BIC   logLik deviance df.resid 
##    120.0    134.7    -56.0    112.0      289 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1211   0.348   
## Number of obs: 293, groups:  Transmitter, 17
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0814 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.10990    0.08870  -1.239    0.215    
## Spawnyes     0.17134    0.03597   4.763  1.9e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus4$KUD95 ~ data_epinephelus_marginatus4$Spawn)

#################################################################################
data_gadus_morhua1 <- subset(week_kuds, File == "Gadus_morhua1")

glmm_gadus_morhua1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua1, family = Gamma(link="log"))
summary(glmm_gadus_morhua1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua1
## 
##      AIC      BIC   logLik deviance df.resid 
##    307.2    328.8   -149.6    299.2     1631 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03343  0.1828  
## Number of obs: 1635, groups:  Transmitter, 60
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0566 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.01418    0.02752   0.515    0.606    
## Spawnyes     0.07521    0.01442   5.217 1.82e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua1$KUD95 ~ data_gadus_morhua1$Spawn)

#################################################################################
data_gadus_morhua2 <- subset(week_kuds, File == "Gadus_morhua2")

glmm_gadus_morhua2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua2, family = Gamma(link="log"))
summary(glmm_gadus_morhua2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua2
## 
##      AIC      BIC   logLik deviance df.resid 
##    197.1    217.2    -94.5    189.1     1132 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04499  0.2121  
## Number of obs: 1136, groups:  Transmitter, 56
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0684 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.004647   0.037490  -0.124    0.901
## Spawnyes    -0.014876   0.024961  -0.596    0.551
boxplot(data_gadus_morhua2$KUD95 ~ data_gadus_morhua2$Spawn)

#################################################################################
data_gadus_morhua3 <- subset(week_kuds, File == "Gadus_morhua3")

glmm_gadus_morhua3 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua3, family = Gamma(link="log"))
summary(glmm_gadus_morhua3)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua3
## 
##      AIC      BIC   logLik deviance df.resid 
##   -741.7   -725.8    374.9   -749.7      395 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.004891 0.06994 
## Number of obs: 399, groups:  Transmitter, 29
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0111 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.17004    0.01833  -9.278  < 2e-16 ***
## Spawnyes     0.04018    0.01253   3.206  0.00135 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua3$KUD95 ~ data_gadus_morhua3$Spawn)

#################################################################################
data_labrus_bergylta <- subset(week_kuds, File == "Labrus_bergylta")

glmm_labrus_bergylta <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_labrus_bergylta, family = Gamma(link="log"))
summary(glmm_labrus_bergylta)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_labrus_bergylta
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2897.4  -2878.7   1452.7  -2905.4      789 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.001729 0.04158 
## Number of obs: 793, groups:  Transmitter, 25
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00195 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.193686   0.008599 -22.523  < 2e-16 ***
## Spawnyes     0.022007   0.003162   6.959 3.42e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_labrus_bergylta$KUD95 ~ data_labrus_bergylta$Spawn)

#################################################################################
data_lichia_amia<- subset(week_kuds, File == "Lichia_amia")

glmm_lichia_amia <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_lichia_amia, family = Gamma(link="log"))
summary(glmm_lichia_amia)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_lichia_amia
## 
##      AIC      BIC   logLik deviance df.resid 
##     87.5     92.8    -39.7     79.5       24 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev. 
##  Transmitter (Intercept) 1.717e-11 4.144e-06
## Number of obs: 28, groups:  Transmitter, 1
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0735 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.9167     0.1356   6.762 1.36e-11 ***
## Spawnyes      0.4826     0.1464   3.296  0.00098 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_lichia_amia$KUD95 ~ data_lichia_amia$Spawn)

#################################################################################
data_pagrus_pagrus1 <- subset(week_kuds, File == "Pagrus_pagrus1")

glmm_pagrus_pagrus1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus1, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -192.5   -174.8    100.3   -200.5      614 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.05044  0.2246  
## Number of obs: 618, groups:  Transmitter, 20
## 
## Dispersion estimate for Gamma family (sigma^2): 0.048 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -0.08623    0.05379  -1.603  0.10888   
## Spawnyes     0.04813    0.01858   2.591  0.00957 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus1$KUD95 ~ data_pagrus_pagrus1$Spawn)

#################################################################################
data_pagrus_pagrus2 <- subset(week_kuds, File == "Pagrus_pagrus2")

glmm_pagrus_pagrus2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus2, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus2
## 
##      AIC      BIC   logLik deviance df.resid 
##     17.3     23.6     -4.6      9.3       32 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.7809   0.8837  
## Number of obs: 36, groups:  Transmitter, 5
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0428 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.46922    0.40388   1.162    0.245
## Spawnyes     0.02786    0.07300   0.382    0.703
boxplot(data_pagrus_pagrus2$KUD95 ~ data_pagrus_pagrus2$Spawn)

#################################################################################
data_pomatomus_saltatrix <- subset(week_kuds, File == "Pomatomus_saltatrix")

glmm_pomatomus_saltatrix <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pomatomus_saltatrix, family = Gamma(link="log"))
summary(glmm_pomatomus_saltatrix)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pomatomus_saltatrix
## 
##      AIC      BIC   logLik deviance df.resid 
##    622.8    635.1   -307.4    614.8      155 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.05284  0.2299  
## Number of obs: 159, groups:  Transmitter, 8
## 
## Dispersion estimate for Gamma family (sigma^2): 0.404 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  1.10622    0.10263  10.778   <2e-16 ***
## Spawnyes    -0.05879    0.13605  -0.432    0.666    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pomatomus_saltatrix$KUD95 ~ data_pomatomus_saltatrix$Spawn)

#################################################################################
data_pseudocaranx_dentex <- subset(week_kuds, File == "Pseudocaranx_dentex")

glmm_pseudocaranx_dentex <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pseudocaranx_dentex, family = Gamma(link="log"))
summary(glmm_pseudocaranx_dentex)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pseudocaranx_dentex
## 
##      AIC      BIC   logLik deviance df.resid 
##   1718.0   1739.3   -855.0   1710.0     1523 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1472   0.3837  
## Number of obs: 1527, groups:  Transmitter, 31
## 
## Dispersion estimate for Gamma family (sigma^2): 0.143 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.10946    0.07357   1.488  0.13681   
## Spawnyes     0.06570    0.02074   3.168  0.00154 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pseudocaranx_dentex$KUD95 ~ data_pseudocaranx_dentex$Spawn)

#################################################################################
data_sciaena_umbra1 <- subset(week_kuds, File == "Sciaena_umbra1")

glmm_sciaena_umbra1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra1, family = Gamma(link="log"))
summary(glmm_sciaena_umbra1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -523.4   -512.0    265.7   -531.4      125 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.008098 0.08999 
## Number of obs: 129, groups:  Transmitter, 15
## 
## Dispersion estimate for Gamma family (sigma^2): 0.000979 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.178216   0.024450  -7.289 3.12e-13 ***
## Spawnyes    -0.009497   0.008092  -1.174    0.241    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra1$KUD95 ~ data_sciaena_umbra1$Spawn)

#################################################################################
data_sciaena_umbra2 <- subset(week_kuds, File == "Sciaena_umbra2")

glmm_sciaena_umbra2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra2, family = Gamma(link="log"))
summary(glmm_sciaena_umbra2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra2
## 
##      AIC      BIC   logLik deviance df.resid 
##     16.2     18.8     -4.1      8.2       10 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 4.245e-12 2.06e-06
## Number of obs: 14, groups:  Transmitter, 1
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0555 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.48445    0.08901   5.443 5.25e-08 ***
## Spawnyes    -0.29053    0.12588  -2.308    0.021 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra2$KUD95 ~ data_sciaena_umbra2$Spawn)

#################################################################################
data_scorpaena_porcus <- subset(week_kuds, File == "Scorpaena_porcus")

glmm_scorpaena_porcus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_porcus, family = Gamma(link="log"))
summary(glmm_scorpaena_porcus)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_porcus
## 
##      AIC      BIC   logLik deviance df.resid 
##   -143.2   -135.4     75.6   -151.2       48 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0005034 0.02244 
## Number of obs: 52, groups:  Transmitter, 13
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00441 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.17716    0.01605 -11.038   <2e-16 ***
## Spawnyes    -0.04611    0.02097  -2.199   0.0279 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_porcus$KUD95 ~ data_scorpaena_porcus$Spawn)

#################################################################################
data_scorpaena_scrofa1 <- subset(week_kuds, File == "Scorpaena_scrofa1")

glmm_scorpaena_scrofa1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa1, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -202.8   -194.5    105.4   -210.8       54 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.002972 0.05451 
## Number of obs: 58, groups:  Transmitter, 7
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0017 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.18855    0.02615  -7.210 5.61e-13 ***
## Spawnyes    -0.01773    0.01702  -1.042    0.297    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa1$KUD95 ~ data_scorpaena_scrofa1$Spawn)

#################################################################################
data_scorpaena_scrofa2 <- subset(week_kuds, File == "Scorpaena_scrofa2")

glmm_scorpaena_scrofa2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa2, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa2
## 
##      AIC      BIC   logLik deviance df.resid 
##   -361.8   -344.9    184.9   -369.8      504 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.0222   0.149   
## Number of obs: 508, groups:  Transmitter, 11
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0308 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.22672    0.04739  -4.784 1.72e-06 ***
## Spawnyes     0.16755    0.01625  10.313  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa2$KUD95 ~ data_scorpaena_scrofa2$Spawn)

#################################################################################
data_seriola_dumerili <- subset(week_kuds, File == "Seriola_dumerili")

glmm_seriola_dumerili <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_seriola_dumerili, family = Gamma(link="log"))
summary(glmm_seriola_dumerili)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_dumerili
## 
##      AIC      BIC   logLik deviance df.resid 
##   1039.4   1054.9   -515.7   1031.4      352 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06693  0.2587  
## Number of obs: 356, groups:  Transmitter, 8
## 
## Dispersion estimate for Gamma family (sigma^2):  0.2 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.67695    0.10122   6.688 2.26e-11 ***
## Spawnyes     0.42055    0.04952   8.493  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_dumerili$KUD95 ~ data_seriola_dumerili$Spawn)

#################################################################################
data_seriola_rivoliana <- subset(week_kuds, File == "Seriola_rivoliana")

glmm_seriola_rivoliana <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_seriola_rivoliana, family = Gamma(link="log"))
summary(glmm_seriola_rivoliana)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_rivoliana
## 
##      AIC      BIC   logLik deviance df.resid 
##   1065.4   1089.2   -528.7   1057.4     2783 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.01212  0.1101  
## Number of obs: 2787, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0968 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.05972    0.02911  -2.052   0.0402 *  
## Spawnyes     0.04774    0.01209   3.949 7.86e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_rivoliana$KUD95 ~ data_seriola_rivoliana$Spawn)

#################################################################################
data_serranus_atricauda <- subset(week_kuds, File == "Serranus_atricauda")

glmm_serranus_atricauda <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_serranus_atricauda, family = Gamma(link="log"))
summary(glmm_serranus_atricauda)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_atricauda
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3445.6  -3427.7   1726.8  -3453.6      646 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 8.305e-05 0.009113
## Number of obs: 650, groups:  Transmitter, 9
## 
## Dispersion estimate for Gamma family (sigma^2): 0.000474 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.263748   0.003475  -75.89   <2e-16 ***
## Spawnyes    -0.002961   0.001762   -1.68   0.0929 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_atricauda$KUD95 ~ data_serranus_atricauda$Spawn)

#################################################################################
data_serranus_scriba <- subset(week_kuds, File == "Serranus_scriba")

glmm_serranus_scriba <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_serranus_scriba, family = Gamma(link="log"))
summary(glmm_serranus_scriba)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_scriba
## 
##      AIC      BIC   logLik deviance df.resid 
##    -30.5    -25.3     19.3    -38.5       23 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02947  0.1717  
## Number of obs: 27, groups:  Transmitter, 10
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00803 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.116927   0.082518  -1.417    0.156
## Spawnyes    -0.002848   0.063364  -0.045    0.964
boxplot(data_serranus_scriba$KUD95 ~ data_serranus_scriba$Spawn)

#################################################################################
data_solea_senegalensis <- subset(week_kuds, File == "Solea_senegalensis")

glmm_solea_senegalensis <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_solea_senegalensis, family = Gamma(link="log"))
summary(glmm_solea_senegalensis)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_solea_senegalensis
## 
##      AIC      BIC   logLik deviance df.resid 
##    -38.6    -24.7     23.3    -46.6      233 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.05953  0.244   
## Number of obs: 237, groups:  Transmitter, 22
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0456 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.08365    0.05788   1.445    0.148
## Spawnyes    -0.03383    0.03830  -0.883    0.377
boxplot(data_solea_senegalensis$KUD95 ~ data_solea_senegalensis$Spawn)

#################################################################################
data_sparisoma_cretense <- subset(week_kuds, File == "Sparisoma_cretense")

glmm_sparisoma_cretense <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparisoma_cretense, family = Gamma(link="log"))
summary(glmm_sparisoma_cretense)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparisoma_cretense
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1031.2  -1012.6    519.6  -1039.2      765 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.01394  0.1181  
## Number of obs: 769, groups:  Transmitter, 10
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0195 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -0.117408   0.038447  -3.054  0.00226 **
## Spawnyes    -0.002199   0.010253  -0.214  0.83017   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparisoma_cretense$KUD95 ~ data_sparisoma_cretense$Spawn)

#################################################################################
data_sparus_aurata1 <- subset(week_kuds, File == "Sparus_aurata1")

glmm_sparus_aurata1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparus_aurata1, family = Gamma(link="log"))
summary(glmm_sparus_aurata1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -517.9   -506.5    262.9   -525.9      123 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.001269 0.03563 
## Number of obs: 127, groups:  Transmitter, 7
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00129 
## 
## Conditional model:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.2336301  0.0149273 -15.651   <2e-16 ***
## Spawnyes     0.0008848  0.0076277   0.116    0.908    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata1$KUD95 ~ data_sparus_aurata1$Spawn)

#################################################################################
data_sparus_aurata2 <- subset(week_kuds, File == "Sparus_aurata2")

glmm_sparus_aurata2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparus_aurata2, family = Gamma(link="log"))
summary(glmm_sparus_aurata2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata2
## 
##      AIC      BIC   logLik deviance df.resid 
##   1502.0   1522.2   -747.0   1494.0     1129 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.3298   0.5742  
## Number of obs: 1133, groups:  Transmitter, 43
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0997 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.60117    0.09069   6.629 3.37e-11 ***
## Spawnyes     0.02415    0.02409   1.002    0.316    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata2$KUD95 ~ data_sparus_aurata2$Spawn)

#################################################################################
data_sphyraena_viridensis1 <- subset(week_kuds, File == "Sphyraena_viridensis1")

glmm_sphyraena_viridensis1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis1, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis1)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis1
## 
##      AIC      BIC   logLik deviance df.resid 
##    159.8    180.5    -75.9    151.8     1294 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02137  0.1462  
## Number of obs: 1298, groups:  Transmitter, 13
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0778 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.053769   0.043035  -1.249    0.212
## Spawnyes     0.009959   0.015824   0.629    0.529
boxplot(data_sphyraena_viridensis1$KUD95 ~ data_sphyraena_viridensis1$Spawn)

#################################################################################
data_sphyraena_viridensis2 <- subset(week_kuds, File == "Sphyraena_viridensis2")

glmm_sphyraena_viridensis2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis2, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis2)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis2
## 
##      AIC      BIC   logLik deviance df.resid 
##   1765.4   1782.7   -878.7   1757.4      554 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1546   0.3932  
## Number of obs: 558, groups:  Transmitter, 17
## 
## Dispersion estimate for Gamma family (sigma^2): 0.277 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.51837    0.10258   5.053 4.34e-07 ***
## Spawnyes     0.56290    0.04677  12.037  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis2$KUD95 ~ data_sphyraena_viridensis2$Spawn)

#################################################################################
data_spondyliosoma_cantharus <- subset(week_kuds, File == "Spondyliosoma_cantharus")

glmm_spondyliosoma_cantharus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_spondyliosoma_cantharus, family = Gamma(link="log"))
summary(glmm_spondyliosoma_cantharus)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_spondyliosoma_cantharus
## 
##      AIC      BIC   logLik deviance df.resid 
##    142.7    160.7    -67.4    134.7      659 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.05021  0.2241  
## Number of obs: 663, groups:  Transmitter, 21
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0657 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.07551    0.05403   1.398  0.16219   
## Spawnyes    -0.05671    0.02129  -2.664  0.00773 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_spondyliosoma_cantharus$KUD95 ~ data_spondyliosoma_cantharus$Spawn)

#################################################################################
#Glmm KUD50 for each File

data_dentex_dentex1 <- subset(week_kuds, File == "Dentex_dentex1")

glmm_dentex_dentex1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dentex_dentex1, family = Gamma(link="log"))
summary(glmm_dentex_dentex1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2280.3  -2261.7   1144.1  -2288.3      774 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04613  0.2148  
## Number of obs: 778, groups:  Transmitter, 19
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0446 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.43928    0.05150 -27.948  < 2e-16 ***
## Spawnyes     0.08437    0.01557   5.418 6.02e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex1$KUD50 ~ data_dentex_dentex1$Spawn)

#################################################################################
data_dentex_dentex2 <- subset(week_kuds, File == "Dentex_dentex2")

glmm_dentex_dentex2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dentex_dentex2, family = Gamma(link="log"))
summary(glmm_dentex_dentex2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1086.3  -1068.7    547.2  -1094.3      595 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07066  0.2658  
## Number of obs: 599, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.112 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.29424    0.07216 -17.935   <2e-16 ***
## Spawnyes     0.02110    0.02875   0.734    0.463    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex2$KUD50 ~ data_dentex_dentex2$Spawn)

#################################################################################
data_dicentrarchus_labrax1 <- subset(week_kuds, File == "Dicentrarchus_labrax1")

glmm_dicentrarchus_labrax1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax1, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2194.2  -2175.2   1101.1  -2202.2      850 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07913  0.2813  
## Number of obs: 854, groups:  Transmitter, 93
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0766 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.46528    0.03192  -45.91   <2e-16 ***
## Spawnyes    -0.01360    0.05476   -0.25    0.804    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax1$KUD50 ~ data_dicentrarchus_labrax1$Spawn)

#################################################################################
data_dicentrarchus_labrax2 <- subset(week_kuds, File == "Dicentrarchus_labrax2")

glmm_dicentrarchus_labrax2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax2, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax2
## 
##      AIC      BIC   logLik deviance df.resid 
##   -534.6   -516.8    271.3   -542.6      633 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1997   0.4469  
## Number of obs: 637, groups:  Transmitter, 28
## 
## Dispersion estimate for Gamma family (sigma^2): 0.179 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.00901    0.09191 -10.978  < 2e-16 ***
## Spawnyes     0.20774    0.03891   5.338 9.37e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax2$KUD50 ~ data_dicentrarchus_labrax2$Spawn)

#################################################################################
data_diplodus_cervinus <- subset(week_kuds, File == "Diplodus_cervinus")

glmm_diplodus_cervinus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_cervinus, family = Gamma(link="log"))
summary(glmm_diplodus_cervinus)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_cervinus
## 
##      AIC      BIC   logLik deviance df.resid 
##   -184.9   -174.7     96.4   -192.9       90 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.1266   0.3558  
## Number of obs: 94, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0816 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.28557    0.18486  -6.954 3.55e-12 ***
## Spawnyes    -0.13142    0.06793  -1.935    0.053 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_cervinus$KUD50 ~ data_diplodus_cervinus$Spawn)

#################################################################################
data_diplodus_sargus1 <- subset(week_kuds, File == "Diplodus_sargus1")

glmm_diplodus_sargus1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus1, family = Gamma(link="log"))
summary(glmm_diplodus_sargus1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1007.6   -992.1    507.8  -1015.6      347 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06204  0.2491  
## Number of obs: 351, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0617 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.53034    0.06840 -22.372   <2e-16 ***
## Spawnyes     0.00321    0.03104   0.103    0.918    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus1$KUD50 ~ data_diplodus_sargus1$Spawn)

#################################################################################
data_diplodus_sargus2 <- subset(week_kuds, File == "Diplodus_sargus2")

glmm_diplodus_sargus2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus2, family = Gamma(link="log"))
summary(glmm_diplodus_sargus2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2444.7  -2426.7   1226.4  -2452.7      656 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02645  0.1626  
## Number of obs: 660, groups:  Transmitter, 20
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0333 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.48767    0.04036  -36.86   <2e-16 ***
## Spawnyes    -0.04083    0.01751   -2.33   0.0197 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus2$KUD50 ~ data_diplodus_sargus2$Spawn)

#################################################################################
data_diplodus_sargus3 <- subset(week_kuds, File == "Diplodus_sargus3")

glmm_diplodus_sargus3 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus3, family = Gamma(link="log"))
summary(glmm_diplodus_sargus3)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus3
## 
##      AIC      BIC   logLik deviance df.resid 
##   -403.3   -393.7    205.6   -411.3       76 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0006003 0.0245  
## Number of obs: 80, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0104 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.76106    0.01966  -89.58  < 2e-16 ***
## Spawnyes     0.08940    0.02304    3.88 0.000104 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus3$KUD50 ~ data_diplodus_sargus3$Spawn)

#################################################################################
data_diplodus_sargus4 <- subset(week_kuds, File == "Diplodus_sargus4")

glmm_diplodus_sargus4 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus4, family = Gamma(link="log"))
summary(glmm_diplodus_sargus4)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus4
## 
##      AIC      BIC   logLik deviance df.resid 
##  -6143.6  -6122.4   3075.8  -6151.6     1470 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03507  0.1873  
## Number of obs: 1474, groups:  Transmitter, 41
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0185 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.579419   0.030197  -52.30   <2e-16 ***
## Spawnyes     0.001954   0.007205    0.27    0.786    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus4$KUD50 ~ data_diplodus_sargus4$Spawn)

#################################################################################
data_diplodus_sargus5 <- subset(week_kuds, File == "Diplodus_sargus5")

glmm_diplodus_sargus5 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus5, family = Gamma(link="log"))
summary(glmm_diplodus_sargus5)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus5
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3438.5  -3418.4   1723.2  -3446.5     1098 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02855  0.169   
## Number of obs: 1102, groups:  Transmitter, 73
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0532 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.56587    0.02359  -66.39   <2e-16 ***
## Spawnyes     0.03946    0.01575    2.51   0.0122 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus5$KUD50 ~ data_diplodus_sargus5$Spawn)

#################################################################################
data_diplodus_sargus6 <- subset(week_kuds, File == "Diplodus_sargus6")

glmm_diplodus_sargus6 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus6, family = Gamma(link="log"))
summary(glmm_diplodus_sargus6)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus6
## 
##      AIC      BIC   logLik deviance df.resid 
##   -223.1   -216.2    115.5   -231.1       37 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08092  0.2845  
## Number of obs: 41, groups:  Transmitter, 6
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00378 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.60644    0.11761 -13.659   <2e-16 ***
## Spawnyes     0.03602    0.02452   1.469    0.142    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus6$KUD50 ~ data_diplodus_sargus6$Spawn)

#################################################################################
data_diplodus_vulgaris1 <- subset(week_kuds, File == "Diplodus_vulgaris1")

glmm_diplodus_vulgaris1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris1, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -195.6   -188.3    101.8   -203.6       42 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04232  0.2057  
## Number of obs: 46, groups:  Transmitter, 9
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0133 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.60890    0.07275 -22.115   <2e-16 ***
## Spawnyes    -0.05592    0.06168  -0.907    0.365    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris1$KUD50 ~ data_diplodus_vulgaris1$Spawn)

#################################################################################
data_diplodus_vulgaris2 <- subset(week_kuds, File == "Diplodus_vulgaris2")

glmm_diplodus_vulgaris2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris2, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris2
## 
##      AIC      BIC   logLik deviance df.resid 
##    -26.9    -29.3     17.4    -34.9        0 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.002723 0.05218 
## Number of obs: 4, groups:  Transmitter, 2
## 
## Dispersion estimate for Gamma family (sigma^2): 9.14e-06 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.283298   0.036960  -34.72   <2e-16 ***
## Spawnyes    -0.567608   0.003708 -153.06   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris2$KUD50 ~ data_diplodus_vulgaris2$Spawn)

#################################################################################
data_epinephelus_marginatus1 <- subset(week_kuds, File == "Epinephelus_marginatus1")

glmm_epinephelus_marginatus1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus1, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus1
## 
##      AIC      BIC   logLik deviance df.resid 
## -10604.1 -10581.6   5306.0 -10612.1     2051 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.005139 0.07169 
## Number of obs: 2055, groups:  Transmitter, 11
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0109 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.767219   0.021966  -80.45  < 2e-16 ***
## Spawnyes     0.020935   0.004637    4.51 6.34e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus1$KUD50 ~ data_epinephelus_marginatus1$Spawn)

#################################################################################
data_epinephelus_marginatus2 <- subset(week_kuds, File == "Epinephelus_marginatus2")

glmm_epinephelus_marginatus2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus2, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3693.3  -3677.0   1850.6  -3701.3      433 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 4.253e-05 0.006522
## Number of obs: 437, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.000423 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.794331   0.002075  -864.6  < 2e-16 ***
## Spawnyes     0.007375   0.002189     3.4 0.000755 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus2$KUD50 ~ data_epinephelus_marginatus2$Spawn)

#################################################################################
data_epinephelus_marginatus3 <- subset(week_kuds, File == "Epinephelus_marginatus3")

glmm_epinephelus_marginatus3 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus3, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus3)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus3
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1368.5  -1354.8    688.2  -1376.5      223 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0002907 0.01705 
## Number of obs: 227, groups:  Transmitter, 4
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00461 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.785956   0.010985 -162.58  < 2e-16 ***
## Spawnyes     0.023495   0.009119    2.58  0.00999 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus3$KUD50 ~ data_epinephelus_marginatus3$Spawn)

#################################################################################
data_epinephelus_marginatus4 <- subset(week_kuds, File == "Epinephelus_marginatus4")

glmm_epinephelus_marginatus4 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus4, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus4)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus4
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1033.4  -1018.7    520.7  -1041.4      289 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06315  0.2513  
## Number of obs: 293, groups:  Transmitter, 17
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0367 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.67263    0.06362  -26.29  < 2e-16 ***
## Spawnyes     0.11494    0.02389    4.81 1.51e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus4$KUD50 ~ data_epinephelus_marginatus4$Spawn)

#################################################################################
data_gadus_morhua1 <- subset(week_kuds, File == "Gadus_morhua1")

glmm_gadus_morhua1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua1, family = Gamma(link="log"))
summary(glmm_gadus_morhua1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -4354.4  -4332.8   2181.2  -4362.4     1631 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04348  0.2085  
## Number of obs: 1635, groups:  Transmitter, 60
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0697 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.51844    0.03117  -48.72  < 2e-16 ***
## Spawnyes     0.08095    0.01598    5.07 4.08e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua1$KUD50 ~ data_gadus_morhua1$Spawn)

#################################################################################
data_gadus_morhua2 <- subset(week_kuds, File == "Gadus_morhua2")

glmm_gadus_morhua2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua2, family = Gamma(link="log"))
summary(glmm_gadus_morhua2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3787.9  -3767.8   1898.0  -3795.9     1132 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.03061  0.175   
## Number of obs: 1136, groups:  Transmitter, 56
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0486 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.57239    0.03120  -50.40   <2e-16 ***
## Spawnyes    -0.04405    0.02104   -2.09   0.0363 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua2$KUD50 ~ data_gadus_morhua2$Spawn)

#################################################################################
data_gadus_morhua3 <- subset(week_kuds, File == "Gadus_morhua3")

glmm_gadus_morhua3 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua3, family = Gamma(link="log"))
summary(glmm_gadus_morhua3)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua3
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1819.0  -1803.0    913.5  -1827.0      395 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.008528 0.09235 
## Number of obs: 399, groups:  Transmitter, 29
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0158 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.70272    0.02317  -73.47  < 2e-16 ***
## Spawnyes     0.05523    0.01494    3.70 0.000219 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua3$KUD50 ~ data_gadus_morhua3$Spawn)

#################################################################################
data_labrus_bergylta <- subset(week_kuds, File == "Labrus_bergylta")

glmm_labrus_bergylta <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_labrus_bergylta, family = Gamma(link="log"))
summary(glmm_labrus_bergylta)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_labrus_bergylta
## 
##      AIC      BIC   logLik deviance df.resid 
##  -5170.0  -5151.2   2589.0  -5178.0      789 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.002124 0.04609 
## Number of obs: 793, groups:  Transmitter, 25
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00238 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.727118   0.009530 -181.23  < 2e-16 ***
## Spawnyes     0.024741   0.003492    7.08 1.39e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_labrus_bergylta$KUD50 ~ data_labrus_bergylta$Spawn)

#################################################################################
data_lichia_amia<- subset(week_kuds, File == "Lichia_amia")

glmm_lichia_amia <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_lichia_amia, family = Gamma(link="log"))
summary(glmm_lichia_amia)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_lichia_amia
## 
##      AIC      BIC   logLik deviance df.resid 
##      5.2     10.5      1.4     -2.8       24 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev. 
##  Transmitter (Intercept) 1.536e-12 1.239e-06
## Number of obs: 28, groups:  Transmitter, 1
## 
## Dispersion estimate for Gamma family (sigma^2): 0.109 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -0.5626     0.1650  -3.409 0.000652 ***
## Spawnyes      0.2794     0.1782   1.567 0.117030    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_lichia_amia$KUD50 ~ data_lichia_amia$Spawn)

#################################################################################
data_pagrus_pagrus1 <- subset(week_kuds, File == "Pagrus_pagrus1")

glmm_pagrus_pagrus1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus1, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2241.5  -2223.8   1124.8  -2249.5      614 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02536  0.1593  
## Number of obs: 618, groups:  Transmitter, 20
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0413 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.66357    0.03941  -42.22   <2e-16 ***
## Spawnyes     0.03767    0.01717    2.19   0.0283 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus1$KUD50 ~ data_pagrus_pagrus1$Spawn)

#################################################################################
data_pagrus_pagrus2 <- subset(week_kuds, File == "Pagrus_pagrus2")

glmm_pagrus_pagrus2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus2, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus2
## 
##      AIC      BIC   logLik deviance df.resid 
##    -97.9    -91.6     53.0   -105.9       32 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.7335   0.8565  
## Number of obs: 36, groups:  Transmitter, 5
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0373 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -1.083803   0.390730  -2.774  0.00554 **
## Spawnyes     0.007209   0.068040   0.106  0.91562   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus2$KUD50 ~ data_pagrus_pagrus2$Spawn)

#################################################################################
data_pomatomus_saltatrix <- subset(week_kuds, File == "Pomatomus_saltatrix")

glmm_pomatomus_saltatrix <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pomatomus_saltatrix, family = Gamma(link="log"))
summary(glmm_pomatomus_saltatrix)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pomatomus_saltatrix
## 
##      AIC      BIC   logLik deviance df.resid 
##     39.4     51.7    -15.7     31.4      155 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06272  0.2504  
## Number of obs: 159, groups:  Transmitter, 8
## 
## Dispersion estimate for Gamma family (sigma^2): 0.368 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.69788    0.10724  -6.507 7.64e-11 ***
## Spawnyes    -0.06845    0.13018  -0.526    0.599    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pomatomus_saltatrix$KUD50 ~ data_pomatomus_saltatrix$Spawn)

#################################################################################
data_pseudocaranx_dentex <- subset(week_kuds, File == "Pseudocaranx_dentex")

glmm_pseudocaranx_dentex <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pseudocaranx_dentex, family = Gamma(link="log"))
summary(glmm_pseudocaranx_dentex)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pseudocaranx_dentex
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3844.6  -3823.3   1926.3  -3852.6     1523 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08244  0.2871  
## Number of obs: 1527, groups:  Transmitter, 31
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0955 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.53944    0.05554 -27.717  < 2e-16 ***
## Spawnyes     0.07720    0.01681   4.592  4.4e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pseudocaranx_dentex$KUD50 ~ data_pseudocaranx_dentex$Spawn)

#################################################################################
data_sciaena_umbra1 <- subset(week_kuds, File == "Sciaena_umbra1")

glmm_sciaena_umbra1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra1, family = Gamma(link="log"))
summary(glmm_sciaena_umbra1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -889.6   -878.1    448.8   -897.6      125 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.0109   0.1044  
## Number of obs: 129, groups:  Transmitter, 15
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00121 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.704016   0.028254  -60.31   <2e-16 ***
## Spawnyes    -0.010727   0.009007   -1.19    0.234    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra1$KUD50 ~ data_sciaena_umbra1$Spawn)

#################################################################################
data_sciaena_umbra2 <- subset(week_kuds, File == "Sciaena_umbra2")

glmm_sciaena_umbra2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra2, family = Gamma(link="log"))
summary(glmm_sciaena_umbra2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra2
## 
##      AIC      BIC   logLik deviance df.resid 
##    -33.4    -30.8     20.7    -41.4       10 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev. 
##  Transmitter (Intercept) 1.673e-11 4.091e-06
## Number of obs: 14, groups:  Transmitter, 1
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0396 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.16613    0.07522  -15.50   <2e-16 ***
## Spawnyes    -0.20429    0.10638   -1.92   0.0548 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra2$KUD50 ~ data_sciaena_umbra2$Spawn)

#################################################################################
data_scorpaena_porcus <- subset(week_kuds, File == "Scorpaena_porcus")

glmm_scorpaena_porcus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_porcus, family = Gamma(link="log"))
summary(glmm_scorpaena_porcus)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_porcus
## 
##      AIC      BIC   logLik deviance df.resid 
##   -282.9   -275.1    145.4   -290.9       48 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 0.0004771 0.02184 
## Number of obs: 52, groups:  Transmitter, 13
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00659 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.70114    0.01877  -90.63   <2e-16 ***
## Spawnyes    -0.05518    0.02466   -2.24   0.0253 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_porcus$KUD50 ~ data_scorpaena_porcus$Spawn)

#################################################################################
data_scorpaena_scrofa1 <- subset(week_kuds, File == "Scorpaena_scrofa1")

glmm_scorpaena_scrofa1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa1, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -363.4   -355.2    185.7   -371.4       54 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.003692 0.06077 
## Number of obs: 58, groups:  Transmitter, 7
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00229 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.71056    0.02962  -57.75   <2e-16 ***
## Spawnyes    -0.02598    0.01977   -1.31    0.189    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa1$KUD50 ~ data_scorpaena_scrofa1$Spawn)

#################################################################################
data_scorpaena_scrofa2 <- subset(week_kuds, File == "Scorpaena_scrofa2")

glmm_scorpaena_scrofa2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa2, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1820.4  -1803.5    914.2  -1828.4      504 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02351  0.1533  
## Number of obs: 508, groups:  Transmitter, 11
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0371 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.74615    0.04910  -35.57   <2e-16 ***
## Spawnyes     0.16387    0.01782    9.20   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa2$KUD50 ~ data_scorpaena_scrofa2$Spawn)

#################################################################################
data_seriola_dumerili <- subset(week_kuds, File == "Seriola_dumerili")

glmm_seriola_dumerili <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_seriola_dumerili, family = Gamma(link="log"))
summary(glmm_seriola_dumerili)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_dumerili
## 
##      AIC      BIC   logLik deviance df.resid 
##   -169.4   -153.9     88.7   -177.4      352 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04089  0.2022  
## Number of obs: 356, groups:  Transmitter, 8
## 
## Dispersion estimate for Gamma family (sigma^2): 0.202 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.02861    0.08266 -12.444   <2e-16 ***
## Spawnyes     0.42232    0.04948   8.535   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_dumerili$KUD50 ~ data_seriola_dumerili$Spawn)

#################################################################################
data_seriola_rivoliana <- subset(week_kuds, File == "Seriola_rivoliana")

glmm_seriola_rivoliana <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_seriola_rivoliana, family = Gamma(link="log"))
summary(glmm_seriola_rivoliana)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_rivoliana
## 
##      AIC      BIC   logLik deviance df.resid 
##  -9159.6  -9135.8   4583.8  -9167.6     2783 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.006496 0.0806  
## Number of obs: 2787, groups:  Transmitter, 16
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0613 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.667018   0.021514  -77.49  < 2e-16 ***
## Spawnyes     0.028314   0.009618    2.94  0.00324 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_rivoliana$KUD50 ~ data_seriola_rivoliana$Spawn)

#################################################################################
data_serranus_atricauda <- subset(week_kuds, File == "Serranus_atricauda")

glmm_serranus_atricauda <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_serranus_atricauda, family = Gamma(link="log"))
summary(glmm_serranus_atricauda)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_atricauda
## 
##      AIC      BIC   logLik deviance df.resid 
##  -5402.6  -5384.7   2705.3  -5410.6      646 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance  Std.Dev.
##  Transmitter (Intercept) 9.271e-05 0.009629
## Number of obs: 650, groups:  Transmitter, 9
## 
## Dispersion estimate for Gamma family (sigma^2): 0.000501 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.796127   0.003653  -491.7   <2e-16 ***
## Spawnyes    -0.004663   0.001812    -2.6   0.0101 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_atricauda$KUD50 ~ data_serranus_atricauda$Spawn)

#################################################################################
data_serranus_scriba <- subset(week_kuds, File == "Serranus_scriba")

glmm_serranus_scriba <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_serranus_scriba, family = Gamma(link="log"))
summary(glmm_serranus_scriba)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_scriba
## 
##      AIC      BIC   logLik deviance df.resid 
##   -111.0   -105.8     59.5   -119.0       23 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02159  0.1469  
## Number of obs: 27, groups:  Transmitter, 10
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0111 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.72592    0.08615 -20.033   <2e-16 ***
## Spawnyes     0.06062    0.07423   0.817    0.414    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_scriba$KUD50 ~ data_serranus_scriba$Spawn)

#################################################################################
data_solea_senegalensis <- subset(week_kuds, File == "Solea_senegalensis")

glmm_solea_senegalensis <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_solea_senegalensis, family = Gamma(link="log"))
summary(glmm_solea_senegalensis)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_solea_senegalensis
## 
##      AIC      BIC   logLik deviance df.resid 
##   -794.3   -780.5    401.2   -802.3      233 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.04342  0.2084  
## Number of obs: 237, groups:  Transmitter, 22
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0426 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.49352    0.05066 -29.481   <2e-16 ***
## Spawnyes    -0.03657    0.03680  -0.994     0.32    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_solea_senegalensis$KUD50 ~ data_solea_senegalensis$Spawn)

#################################################################################
data_sparisoma_cretense <- subset(week_kuds, File == "Sparisoma_cretense")

glmm_sparisoma_cretense <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparisoma_cretense, family = Gamma(link="log"))
summary(glmm_sparisoma_cretense)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparisoma_cretense
## 
##      AIC      BIC   logLik deviance df.resid 
##  -3587.9  -3569.3   1797.9  -3595.9      765 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.02709  0.1646  
## Number of obs: 769, groups:  Transmitter, 10
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0153 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.662623   0.052688 -31.556   <2e-16 ***
## Spawnyes     0.004549   0.009085   0.501    0.617    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparisoma_cretense$KUD50 ~ data_sparisoma_cretense$Spawn)

#################################################################################
data_sparus_aurata1 <- subset(week_kuds, File == "Sparus_aurata1")

glmm_sparus_aurata1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparus_aurata1, family = Gamma(link="log"))
summary(glmm_sparus_aurata1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata1
## 
##      AIC      BIC   logLik deviance df.resid 
##   -854.6   -843.2    431.3   -862.6      123 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.001468 0.03831 
## Number of obs: 127, groups:  Transmitter, 7
## 
## Dispersion estimate for Gamma family (sigma^2): 0.00199 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.768618   0.016448 -107.53   <2e-16 ***
## Spawnyes     0.003994   0.009427    0.42    0.672    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata1$KUD50 ~ data_sparus_aurata1$Spawn)

#################################################################################
data_sparus_aurata2 <- subset(week_kuds, File == "Sparus_aurata2")

glmm_sparus_aurata2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparus_aurata2, family = Gamma(link="log"))
summary(glmm_sparus_aurata2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata2
## 
##      AIC      BIC   logLik deviance df.resid 
##  -2100.7  -2080.6   1054.3  -2108.7     1129 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.2786   0.5279  
## Number of obs: 1133, groups:  Transmitter, 43
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0996 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.01828    0.08383 -12.147   <2e-16 ***
## Spawnyes     0.05459    0.02412   2.264   0.0236 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata2$KUD50 ~ data_sparus_aurata2$Spawn)

#################################################################################
data_sphyraena_viridensis1 <- subset(week_kuds, File == "Sphyraena_viridensis1")

glmm_sphyraena_viridensis1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis1, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis1)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis1
## 
##      AIC      BIC   logLik deviance df.resid 
##  -4622.5  -4601.8   2315.2  -4630.5     1294 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.009475 0.09734 
## Number of obs: 1298, groups:  Transmitter, 13
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0478 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.68092    0.02922  -57.53   <2e-16 ***
## Spawnyes     0.02217    0.01240    1.79   0.0739 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis1$KUD50 ~ data_sphyraena_viridensis1$Spawn)

#################################################################################
data_sphyraena_viridensis2 <- subset(week_kuds, File == "Sphyraena_viridensis2")

glmm_sphyraena_viridensis2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis2, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis2)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis2
## 
##      AIC      BIC   logLik deviance df.resid 
##   -292.5   -275.2    150.2   -300.5      554 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.09615  0.3101  
## Number of obs: 558, groups:  Transmitter, 17
## 
## Dispersion estimate for Gamma family (sigma^2): 0.219 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.06979    0.08232 -12.995  < 2e-16 ***
## Spawnyes     0.27259    0.04106   6.639 3.16e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis2$KUD50 ~ data_sphyraena_viridensis2$Spawn)

#################################################################################
data_spondyliosoma_cantharus <- subset(week_kuds, File == "Spondyliosoma_cantharus")

glmm_spondyliosoma_cantharus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_spondyliosoma_cantharus, family = Gamma(link="log"))
summary(glmm_spondyliosoma_cantharus)
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_spondyliosoma_cantharus
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1988.4  -1970.4    998.2  -1996.4      659 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.06214  0.2493  
## Number of obs: 663, groups:  Transmitter, 21
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0586 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.46186    0.05876 -24.877  < 2e-16 ***
## Spawnyes    -0.05414    0.02015  -2.687  0.00721 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_spondyliosoma_cantharus$KUD50 ~ data_spondyliosoma_cantharus$Spawn)

#################################################################################
#Exploratory analysis
plot(week_kuds$Week, week_kuds$KUD95)

glm_week <- glm(KUD95 ~ Week, data = week_kuds, family=Gamma(link="log"))
with(week_kuds, plot(KUD95 ~ Week, pch = 1, col="deepskyblue"))
seq <- levels(week_kuds$Week)
predictweek <- predict(glm_week,newdata=data.frame(Week=seq), type="response")
lines(seq, predictweek, lty=1, col="red")
## Warning in xy.coords(x, y): NAs introduced by coercion

week_kuds$Week <- as.numeric(week_kuds$Week)

##See how KUD varies over Weeks by Spawning season
week_kuds_ss <- subset(week_kuds, SpawnSeason == "SS")
week_kuds_a <- subset(week_kuds, SpawnSeason == "A")
week_kuds_w <- subset(week_kuds, SpawnSeason == "W")

grid.arrange(
ggplot(week_kuds_ss, aes(x = Week, y = KUD95)) +
  geom_point(col = "green") +
  labs(title = "KUD95 over Weeks SS",
       x = "Week",
       y = "KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal() ,


ggplot(week_kuds_a, aes(x = Week, y = KUD95)) +
  geom_point(col = "red") +
  labs(title = "KUD95 over Weeks A",
       x = "Week",
       y = "KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal() ,

ggplot(week_kuds_w, aes(x = Week, y = KUD95)) +
  geom_point(col = "blue") +
  labs(title = "KUD95 over Weeks W",
       x = "Week",
       y = "KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal()
)

#With predictions
#SS
gam_ss <- gam(KUD95 ~ s(Week), data = week_kuds_ss[week_kuds_ss$SpawnSeason == "SS", ], family = Gamma(link = "log"))
summary(gam_ss)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## KUD95 ~ s(Week)
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.065008   0.004294   15.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##           edf Ref.df    F p-value    
## s(Week) 7.774  8.451 14.3  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.0049   Deviance explained =  1.2%
## GCV = 0.18719  Scale est. = 0.41752   n = 22642
week_kuds_ss$predicted <- predict(gam_ss, newdata = week_kuds_ss, type = "response")

plotss<- ggplot(week_kuds_ss, aes(x = Week, y = KUD95)) +
  geom_point(col = "green") +
  geom_line(aes(y = predicted), col = "black", linetype = "dashed") +
  labs(title = "KUD95 over Weeks (SpawnSeason = SS)",
       x = "Week",
       y = "KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal()

#SA
gam_a <- gam(KUD95 ~ s(Week), data = week_kuds_a[week_kuds_a$SpawnSeason == "A", ], family = Gamma(link = "log"))
summary(gam_a)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## KUD95 ~ s(Week)
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.37619    0.01799   20.91   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##           edf Ref.df     F p-value
## s(Week) 4.532  5.558 1.304   0.252
## 
## R-sq.(adj) =  0.00297   Deviance explained =  1.1%
## GCV = 0.26384  Scale est. = 0.43226   n = 1335
week_kuds_a$predicted <- predict(gam_a, newdata = week_kuds_a, type = "response")

plota <- ggplot(week_kuds_a, aes(x = Week, y = KUD95)) +
  geom_point(col = "red") +
  geom_line(aes(y = predicted), col = "black", linetype = "dashed") +
  labs(title = "KUD95 over Weeks (SpawnSeason = A)",
       x = "Week",
       y = "KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal()

#W
gam_w <- gam(KUD95 ~ s(Week), data = week_kuds_w[week_kuds_w$SpawnSeason == "W", ], family = Gamma(link = "log"))
summary(gam_w)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## KUD95 ~ s(Week)
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.43817    0.01598   27.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##          edf Ref.df     F p-value    
## s(Week) 8.48  8.923 34.47  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.131   Deviance explained =   21%
## GCV = 0.31676  Scale est. = 0.4175    n = 1635
week_kuds_w$predicted <- predict(gam_a, newdata = week_kuds_w, type = "response")

plotw <- ggplot(week_kuds_w, aes(x = Week, y = KUD95)) +
  geom_point(col = "blue") +
  geom_line(aes(y = predicted), col = "black", linetype = "dashed") +
  labs(title = "KUD95 over Weeks (SpawnSeason = w)",
       x = "Week",
       y = "KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal()

grid.arrange(plotss, plota, plotw)

##########################################################################################################
week_kuds$Week <- as.numeric(week_kuds$Week)

#Model that describes KUD95 over Week by Spawning season
gam_model <- gam(KUD95 ~ s(Week, by = SpawnSeason), data = week_kuds, family = Gamma(link = "log"))
summary(gam_model)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## KUD95 ~ s(Week, by = SpawnSeason)
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.10958    0.00418   26.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                         edf Ref.df      F p-value    
## s(Week):SpawnSeasonA  6.476  7.311  7.661  <2e-16 ***
## s(Week):SpawnSeasonSS 7.661  8.374 13.125  <2e-16 ***
## s(Week):SpawnSeasonW  8.373  8.649 28.859  <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.0293   Deviance explained = 4.29%
## GCV = 0.21295  Scale est. = 0.44276   n = 25612
#Make predictions of the model
new_data <- data.frame(Week = rep(seq(min(week_kuds$Week), max(week_kuds$Week), length.out = 100), times = nlevels(week_kuds$SpawnSeason)),
                       SpawnSeason = factor(rep(levels(week_kuds$SpawnSeason), each = 100)))

new_data$predicted_KUD95 <- predict(gam_model, new_data, type = "response")

ggplot(new_data, aes(x = Week, y = predicted_KUD95, color = SpawnSeason)) +
  geom_line() +
  labs(title = "Predicted KUD95 over Weeks by Spawning Season",
       x = "Week",
       y = "Predicted KUD95") +
  scale_y_continuous(limits = c(0, 15)) +
  theme_minimal()

week_kuds$Week <- as.factor(week_kuds$Week)

plot(week_kuds$Week, week_kuds$KUD50)

glm_week1 <- glm(KUD50 ~ Week, data = week_kuds, family=Gamma(link="log"))
with(week_kuds, plot(KUD50 ~ Week, pch = 1, col="deepskyblue"))
seq1 <- levels(week_kuds$Week)
predictweek1 <- predict(glm_week1,newdata=data.frame(Week=seq1), type="response")
lines(seq1, predictweek1, lty=1, col="red")

glmm_spawn <- glmmTMB(KUD95 ~ Spawn + (1|Species), data = week_kuds, family = Gamma(link="log")) 
summary(glmm_spawn)
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Spawn + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##  25460.0  25492.6 -12726.0  25452.0    25608 
## 
## Random effects:
## 
## Conditional model:
##  Groups  Name        Variance Std.Dev.
##  Species (Intercept) 0.1492   0.3862  
## Number of obs: 25612, groups:  Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.148 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 0.122603   0.071420   1.717    0.086 .  
## Spawnyes    0.065522   0.005057  12.957   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lm <- lm(KUD95 ~ Spawn * Species, data = week_kuds)
boxplot(KUD95 ~ Spawn, data = week_kuds)